It is important that a power-assist exoskeleton robot
automatically assists the user’s motion according to
that motion intention in real time. Electromyographic
(EMG) signals — which are generated when muscles are
activated — are one of the most important biological
signals to determine the user’s motion. The amount of
the EMG signal indicates the muscle activity level (i.e.,
the amount of generating force) and it can be easily
measured using simple electrodes.

If the amount of generating force by certain
muscles is estimated, the amount of user’s joint torque
can also be estimated (see Figure 1). Therefore, it can be
used to activate the power-assist exoskeleton robot
automatically, since it directly reflects the intention of
the user. Consequently, human motion can be estimated
if the amount of muscle force of certain muscles is
estimated. If the user’s motion is estimated in real-time,
the motion can be easily assisted by the exoskeleton.
The EMG-based control is not very easy to be realized,
however, because of several reasons.

In this article, EMG-based control methods for
power-assist exoskeleton robots will be introduced. Soft
computing technologies such as fuzzy reasoning, neural
networks, or genetic algorithms are powerful tools to
make the robot system intelligent. They can be applied
to develop an effective EMG-based controller for power-assist exoskeleton robots. We will discuss, two kinds of
EMG-based control methods in which soft computing
technologies are introduced and applied.

The Geometry of Power-Assist

Because the power-assist exoskeleton robot is
supposed to be directly attached to the user’s body, the
design condition of the robot architecture is restricted in
comparison with that of ordinal robots. The actuators,
sensors, links, and frames of the exoskeleton must all be
located outside of the user’s body and not disturb the
user’s motion under any configuration. Moreover, the
weight of the exoskeleton should not be directly
supported by the user’s body.

Therefore, the hardware design is harder to come
up with than that of ordinary robots. Development of
small-size, light-weight, and high-power actuators such